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Capital Allocation

CommunityPopular
cbrock84
capital-allocation

Evaluates where to spend limited capital — investment appraisal, hurdle rates, payback, and comparing proposals that are not alike. Use this to evaluate an investment or major purchase, compare competing funding requests, set a hurdle rate, decide between building and buying, or review whether past investments delivered what was claimed.

Overview

Publishercbrock84
Repositoryheadcount
Skill namecapital-allocation
Stars
1.6K
Forks
237
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cbrock84 on GitHub. Read the source before you install it.

Installation

Install the Capital Allocation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cbrock84/headcount.git /tmp/headcount
mkdir -p .claude/skills
cp -r /tmp/headcount/plugins/finance/skills/capital-allocation .claude/skills/capital-allocation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Capital Allocation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Capital Allocation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Capital Allocation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Capital allocation

Choosing among investments is choosing what not to do. This is the appraisal of individual proposals; which businesses and bets the company should be in belongs to corporate-strategy:portfolio-strategy.

Appraise on incremental cash

Only cash flows that change because of the decision belong in the analysis:

  • Sunk costs are irrelevant. Money already spent is not a reason to continue, though it is reliably presented as one.
  • Allocated overhead is usually irrelevant. If the cost occurs anyway, it does not belong in the incremental case.
  • Opportunity cost is relevant, including the capacity consumed that then cannot serve anything else.
  • Working capital is a real outflow. Growth that consumes cash is not free because it is growth.

Discount for time and risk. A hurdle rate should reflect the risk of the specific proposal — applying one company-wide rate systematically overfunds risky projects and starves safe ones.

Read payback for what it tells you

Payback ignores everything after the threshold and so is a poor ranking tool. It is a good liquidity and uncertainty measure: how long capital is at risk, and how far into an uncertain future the case depends on.

Use net present value to decide, payback to understand exposure. A proposal with strong NPV whose returns all arrive in years four and five is a forecasting question as much as an investment one.

Interrogate the case, not the sponsor

Every proposal arrives advocated for. The useful questions are structural:

  • What has to be true for this to work, and which of those is least certain?
  • What is the counterfactual — what happens if we do nothing?
  • Where is the optionality: can it be staged so a small commitment buys information before the large one?
  • Who is accountable for the benefit after approval?

Stage-gating dominates all-or-nothing commitment where uncertainty is high. Paying for information first is usually cheaper than being right by luck.

Look back, or the numbers stay fictional

Compare realized outcomes against the approved case, and make it routine rather than punitive. Where nobody looks back, forecasts drift optimistic because optimism is rewarded at approval and never tested afterwards.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Never

  • Include sunk cost in a forward case.
  • Apply one hurdle rate to proposals of different risk.
  • Rank by payback.
  • Approve a benefit with no owner after approval.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Capital Allocation AI skill do?

Evaluates where to spend limited capital — investment appraisal, hurdle rates, payback, and comparing proposals that are not alike. Use this to evaluate an investment or major purchase, compare competing funding requests, set a hurdle rate, decide between building and buying, or review whether past investments delivered what was claimed.

Why use Capital Allocation on TypingMind?

Because you install it once and use it with any model. Capital Allocation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Capital Allocation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cbrock84/headcount/tree/main/plugins/finance/skills/capital-allocation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Capital Allocation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Capital Allocation?

As many as you like. As long as a model supports skills, you can use Capital Allocation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Capital Allocation AI skill free?

Yes. It is published on GitHub by cbrock84 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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